Euphytica 91 : 89-97, 1996 .

89© 1996 Kluwer Academic Publishers. Printed in the Netherlands .

Mapping QTLs in breeding for drought tolerance in maize (Zea mays L .)

Hesham A. S. Agrama l * & Mounir E. Moussa2'Maize Research Department, Agricultural Research Centre, Field Crops Research Institute, Egypt ; 2Departmentof Genetics, Faculty of Agriculture, Alexandria University, Egypt ; (*author for correspondence)

Received 11 August 1995 ; accepted 15 March 1996

Key words: anthesis-silking interval, drought tolerance, grain yield, maize, QTL, RFLP,Zea mays

Summary

Grain yield in the maize (Zea mays L) plant is sensitive to drought in the period three weeks either side of flowering .Maize is well-adapted to the use of restriction fragment length polymorphisms (RFLPs) to identify a tight linkagebetween gene(s) controlling the quantitative trait and a molecular marker . We have determined the chromosomallocations of quantitative trait loci (QTLs) affecting grain yield under drought, anthesis-silking interval, and numberof ears per plant. The F3 families derived from the cross SD34(tolerant) x SD35(intolerant) were evaluated for thesetraits in a two replicated experiment . RFLP analysis of the maize genome included non-radioactive DNA-DNAhybridization detection using chemiluminescence. To identify QTLs underlying tolerance to drought, the meanphenotypic performances of F3 families were compared based on genotypic classification at each of 70 RFLP markerloci. The genetic linkage map assembled from these markers was in good agreement with previously publishedmaps. The phenotypic correlations between yield and other traits were highly significant . In the combined analyses,genomic regions significantly affecting tolerance to drought were found on chromosomes 1,3,5,6, and 8 . For yield,a total of 50% of the phenotypic variance could be explained by five putative QTLs . Different types of gene actionwere found for the putative QTLs for the three traits .

Introduction

Interaction between water utilization and grain yieldof maize (Zea mays L.) has been studied by var-ious researchers, often with similar results . It hasbeen observed that grain yield is significantly reducedwhen plants are grown under water shortage (Moss &Downey, 1971; Hall et al ., 1981 ; Quattar et al ., 1987 ;Sobrado, 1990 ; Attia et al., 1994) . Because maize isproduced in areas of sub-optimal rainfall or water irri-gation, additional yield increases may be achieved byselecting for genotypes with greater plant productivityunder limited soil moisture (Boyer, 1982 & Landi etal., 1995) . The effects of water stress on grain yielddepend upon the degree, duration and timing of thedrought conditions. It has been reported, that the mostsensitive period is just before and during flowering(Herrero & Johnson, 1981 ; Westgate & Boyer, 1985) .Recent studies have given more attention to flower-ing traits, such as the synchronization of the male and

female flowering (anthesis-silking interval), and oth-ers, such as number of ears per plant under drought(Edmeades et al ., 1990; Guei & Wassom, 1992) . Thesemay be among the most important characters in select-ing for drought tolerance in maize . All these traits ofagronomic importance to drought tolerance show con-tinuous variation due to polygenic inheritance and theinfluence of environmental factors . Trait expressionis likely to be regulated by a great number of genes,which differ in magnitude and direction of their effectsand may interact with each other .

Reports on linkage between quantitative traiteffects and major genes (Rasmusson, 1933 ; Everson& Schaller, 1955 ; Thoday, 1961) followed one of theearliest such reports by Sax (1923) . Practical applica-tion of this approach, however, was limited due to lackof suitable markers. Germplasm carrying specific traitscan be identified by molecular techniques, especiallythrough the use of restriction fragment length polymor-phisms (RFLPs). The advent of methods for detecting

90

Table 1 . Quantitative traits measured on the F3 generation of SD34 x SD35

Trait

Description

GYD Grain yield at harvest under limited water regimes, adjusted to a standard grain moisture 14 .5% (single plot measurement)DTP

Number of days from planting to 50% of F 3 individuals shedding pollen (single plot measurement)DTS

Number of days from planting to 50% of F 3 individuals silking (single plot measurement)ASI

Number of days difference between 50% pollen shedding to 50% silking (single plot measurement)EAR

Number of ears per plant, calculated from the number of ears and the number of plants in a plot (single plot measurement)TBN

Number of tassel branches, including main and secondary branches (plot average based on 6 individual plants)PHT

Distance between ground and tassel tip at maturity (plot average based on 6 individual plants)EHT

Distance between ground and top ear node at maturity (plot average based on 6 individual plants)

RFLPs has resulted in the effective coverage of manygenomes such as maize (Hoisington & Coe, 1990),tomato (Bernatzky & Tanksley, 1986), lettuce (Landryet al ., 1987), Brassica oleracea (Slocum et al., 1990),and soybean (Keim et al ., 1990). A further improve-ment was the development of statistical methods han-dling linkage analysis between marker loci and QTLsfor experimental populations used by plant breeders(Lander & Botstein 1989) . One of the more promisinguses of RFLP in plant breeding is the identification andmanipulation of genetic factors underlying agronomictraits ; particularly those that show quantitative inheri-tance (QTLs) . Many quantitative traits in various cropspecies have been studied using RFLP-drived geneticmaps, e.g. tomato (Paterson et al ., 1988), and maize(Stuber, 1992 ; Edwards et al., 1987) . There are only afew reports in which molecular markers have actual-ly been used for mapping QTLs for drought tolerancein maize (Leberton et al ., 1995 ; Ribaut et al ., 1995) .Indirect selection for marker loci linked to QTLs ofinterest could enhance selection efficiency (Nienhuiset al., 1987) . The localization of specific QTLs mayalso be the first step in eventual cloning of loci under-lying quantitative trait variation .

The objectives of this study were to (i) identify theRFLP markers associated with QTLs affecting grainyield, prolificacy and flowering under drought stressin maize, (ii) estimate the relative effect and types ofgene action exhibited by identified QTLs .

Materials and methods

Experimental plant material

Eight inbred lines were chosen from the current maize(Zea mays L.) breeding program of the AgriculturalResearch Center, Egypt. The maize lines were grown

in replicated yield trials during the summer of 1991at Nubaria Agricultural Research Station under with-drawal of irrigation at tassel initiation (3 weeks beforeflowering) to induce flowering and grain-filling stress.In the same season in an unstressed nursery, all pos-sible crosses were made between the lines to produceFt hybrids. Two lines, SD34 and SD35, were identi-fied as being tolerant and intolerant, respectively, underdrought stress . The resultant Ft cross SD34 x SD35 wasselfed in the summer nursery in 1992 and about 300F2 plants were selfed in the field the following summer(1993) to produce F3 families .

Phenotypic evaluation of quantitative traits

Two hundred and thirty of the SD34 x SD35 F3 familieswere grown under an irrigation regime in the summerof 1994 in a randomized block design . There weretwo complete blocks in each of two replications ofthe experiment; thus, each F3 family was representedby twenty plants in one-row plot replicated over timeunder the similar environmental conditions at Alexan-dria University Agricultural Research Station . How-ever, not all F2 individuals produced enough F3 seed,resulting in fewer F3 families phenotyped than F2 indi-viduals . The phenotypes of the F3 were obtained eitherfrom single plant or plot measurements (Table 1) . Traitsthought to be indicative of plant drought resistance(Edmeades et al., 1990) were measured . Only about120 F2 individuals whose F3 average grain yield wasamong the most extreme phenotypes (high and lowyield) were genotyped with RFLPs (Lander & Bot-stein, 1989) .

RFLP analysis

DNA was extracted from lyophilized leaf tissue of 300F2 plants following a modification of the CTAB method

of Saghai-Maroof et al . (1984) . Genomic DNA of eachindividual was digested with the restriction endonu-cleases Eco RI, Hind III, and Bam HI . Digested DNAwas fractioned in 0 .7% agarose gels on a custom madetray of 4 tiers (30 lanes each) . Up to 120 DNA sam-ples can be loaded in a single gel . After electrophore-sis, gels were denatured, neutralized and Southernblotted onto uncharged nylon membranes using stan-dard techniques. DNA probes were labeled by Poly-merase Chain Reaction (PCR) amplification with 2.5%or 5.0% digoxigenin d-UTP (Boehringer-Mannheim) .The probes were detected according to the chemilu-minescent protocol described in detail by Hoisington(1992) and Ragot & Hoisington (1993) . RFLP probeswere obtained from Brookhaven National Laboratory(bnl) and University of Missouri at Columbia (umc) .

Linkage analysis and QTL mapping

A set of 116 clones were screened against the two par-ents (SD34 and SD35) and a F2 progeny sample of sixindividuals . The polymorphic RFLP probes betweenthe two parents and segregating in the progeny wereidentified . A total of 72 probes were selected duringthis step and used on the mapping population . Pair-wise and multipoint linkage analysis were performedwith the MAPMAKER program developed by Landeret al . (1987) . RFLP loci were mapped with respect toeach other based on both linkage analyses and previousknowledge (E.H. Coe, personal communication MNL68, 1994) . Map distances (cM) were then estimatedusing recombination distances and Kosambi's mappingfunction (1944) between ordered marker loci . Becauseof selective genotyping of F3 families based on grainyield (GYD), only traits that showed a phenotypic cor-relation of at least 0 .4 (P<0.001) with GYD were ana-lyzed. QTL likelihood maps were constructed by themethod of interval mapping using MAPMAKER/QTL(Lander & Botstein, 1989), and are represented as QTLlikelihood plots (Paterson et al ., 1988 & 1991) . A LODscore threshold of 3 .0 was suggested Stuber et al .(1992)to declare the existence of a QTL (Stuber et al ., 1992) .For the F2 generation, Schon et al . (1993) pointed outthat if the progeny means of F3 lines instead of F2 indi-viduals are used for phenotyping, then only half of thedominance effects contribute to the genotypic mean ofF3 lines derived from heterozygous F2 plants . There-fore, estimates of d from MAPMAKER/QTL weremultiplied by two in this study to obtain the usual esti-mates of dominance effects according to the F m-metric(Mather & Jinks, 1971) . Given the genetic length of

9 1

Grain yield under drought (t/h )

Figure 1 . Frequency distribution of the parents and F3 family grainyield (th - 1 ) under drought .

the maize genome and the density of markers used,the threshold was reduced to 2 .2. The later thresholdgives a probability of less than 5% that a single falsepositive will occur anywhere in the genome . This isapproximately equivalent to requiring the significancelevel for a single test to be 0 .001 . Finally, estimatesof additive and dominance effects, the total varianceexplained by significant QTLs, and the total LOD scorewere obtained by fitting a model including all putativeQTLs for the respective trait simultaneously .

Results

Drought response and phenotypic correlations

In the present study, significant differences for all traitswere found among F3 families in the two replicationsof the experiment . The interaction between experi-mental replications and families was not significant,thus individual families responded similarly in bothreplicates . The distributions for DTP, DTS, ASI, TBN,PHT, and EHT (Table 1) were approximately normal,while number of ears per plant (EAR) was clearlyskewed toward the lower values. The transformationloglo (EAR) improved normality, and was used inall analyses. The frequency of F3 family grain yieldunder drought is shown in Figure 1 . All of the traitsshowed continuous variation, typical of quantitativetraits . Consistent with previous observations (Fisch-er et al ., 1989; Frederick et al., 1989; Edmeades etal., 1990; Guei & Wassom, 1992), grain yield underdrought was correlated with number of ears per plant,and with tassel size (Table 2) . Yield was negatively

92

1

2

3

4- umc 184

bnI8 .45

- U= 121

- umc 31a12.611.3

umc 53

-bnl5.4631 .4

425.

42.4

-^- una 9216.4

-umc42aumc102 - Um 43

42.1

23.1

41.8

16.8

- umc 167

bnl 5 .37

12.1 umc 40

147

-bnl 5.71.

39.1

- Um 131

-umc 80

26.417.133.9

--ume 18

-_ ume 55 36.5

18.5

- umo 76

- bnl 5.5917.3-umc 58

22.9

--- umc 128

24.2- um 140

- bnl 8 .32

Figure 2 . Genetic linkage map was generated using an F3 population derived from SD34 x SD35. The map included 70 RFLP loci, scored inabout 120 individulas, and linkage analysis was done using MAPMAKER v2 .0 .

26.5- ume6

176- umc 34

42.4

m-- Mlle 96

umc48.0

Um 137

30.1

Um 38

- Meso

22.4- URIC 133

10.9

47.2

- ume52

- bnl 8 .23

Table 2 . Linear correlation coefficients between grain yield underdrought and other traits from field experiments of 1994

*` indicate correlations significant at P<0 .05 and 0 .01, respec-tively ; n.s.- not significant .

correlated with DTS, DTP, and ASI. However, plantand ear heights did not show a significant correlationwith yield.

Segregation of RFLP markers and linkage map

Deviation from the expected 1 :2:1 genotype ratio wassignificant (P<0 .05) for 3 of the 72 markers scored.The three loci were found in regions of chromosome 3(umcl2l, umcl02 and bn15.37) . The coinciding withregion of distorted segregations found on the samechromosome by Lebreton et al ., (1995). Also, dis-torted segregation ratios have been observed in othermarker-based studies (Edwards et al., 1987; Kahler

5bnl 6.25

13.1umc 147

48.5

11 .3 ume 1261.2 -bu1 5 .40

27.7

umc141

-bnl 5.24

26.9

uno 104

6

7

8

9ume 136 _, b,4 9.11

--umc 113a21.6

28.4

30.2

-umc 105--- umc 124 173

379

-- WE lf0 10.7

brA81_ 7.81- ume 65

39.8

8.7 umc21unlc 46

- ume20

120 umc85- ume59

37.3

- Me 1327.5

unq 82

353

--umc120 19.2

---ume16.3-- umc95 22.4

31.5

42.5

_,u,c4,5 -ung9312.6

12.3

umc 117_ umc188

39 .8

tmle 7

48.4

10.0

bul5.09Um 15

10- umc 130

16 .7- UMC 64

-^Um"

& Wehrhahn, 1986 ; Reiter et al ., 1991). Only twoloci, i .e. umc35 and umc114, were not significantlylinked to one of 10 linkage groups . The RLFP link-age map is shown for the F2 population in Figure 2 .The map developed from segregation of the 70 markerloci was generally in agreement with the map present-ed in Maize Genetics Cooperation Newsletter of 1995 .Generally, each probe detected only a single polymor-phism which mapped to a previously known positionin the RFLP maize map. However, locus umc15 wasfound to be linked to chromosome 9 while it was pre-viously mapped on chromosome 4 . This indicates thatprobe umc 15 may detect distinct polymorphic loci indifferent populations .

Identification of QTLs underlying drought tolerance

In the combined analysis across replications, fivegenomic regions located on chromosomes 1, 3, 5,and 8 were found to affect significantly drought tol-erance determined as grain yield under drought (Table3 ; Figure 3). LOD scores at peaks of QTL likelihoodmaps ranged from 3 .3 to 7.1 for the genomic regionson chromosomes 5 and 1, respectively. Parental lineSD34 contributed the alleles for drought tolerance atfour of the five putative QTLs, while one allele con-ferring tolerance on chromosome 3 came from SD35(intolerant parent) . The type of gene action was studied

Characteristic Grain yield under drought

Days to 50% silking -0.302*`Days to 50% pollen shedding -0.318**Number of ears per plant 0 .534*Days of anthesis-silking interval -0.449*Tassel size 0 .261 `Plant height n .s.Ear hieght n .s .

Chromosome 5

50 100 150 CM

using the values of dominance and additive parametersprovided by MAPMAKER/QTL . Estimates of geneticeffects revealed different types of gene action (Table 3) .The putative QTL mapping to chromosome 3 displayedadditive gene action (d = 0) . Interval mapping analysissuggested the presence of two distinct but linked QTLson chromosome 5 which exhibited partial dominance(-a < d < 0) for grain yield under drought. Also, partialdominance was found on chromosome 1, while over-dominance (d < -a) for drought tolerance was foundon chromosome 8 . In total, 49 .6% of the phenotypicvariance of GYD was explained by the five QTLs .

Three genomic regions on chromosomes 1, 3, and 6significantly affected anthesis-silking interval (ASI) inthe analysis across replications (Table 3) . LOD scoresat peaks of likelihood maps were not high, rangingfrom 3 .9 to 4.6 on chromosomes 6 and 3, respectively.For the two putative QTLs on chromosomes 1 and 3,

6

S

4

3

2

I

0

Chromosome 6

50

GYD . - . . ASI - EAR

100 CM

50

S

4

3

2

1

0

100

Chromosome 8

50

150 CM

1100 CM

93

Figure 3. QTL likelihood maps indicating LOD score for grain yield under drought (GYD), anthesis-silking-interval (ASI), and ears per plant(EAR). The horizontal line at a height of 2 .2 indicates the stringent threshold that the LOD score must cross to allow the presence of a QTL tobe inferred .

the alleles from SD35 caused an increase in ASI . Thesetwo QTLs exhibited partial dominance and additivevariation, respectively, whereas overressiveness (d <-a) was determined by the third QTL on chromosome6. For ASI under drought, Ribaut et al . (1995) iden-tified six genomic regions on chromosomes 1,2,5,6,8and 10 . Lebreton et al . (1995) revealed that anthe-sis dates under drought were determined by QTLs onchromosomes 4,6 and 8, while effects on floweringtime have been found previously on chromosome 6 byVeldboom et al. (1994) . For ASI in our results, 38.4%of the total phenotypic variance was explained by thethree putative QTLs .

Three QTLs were identified for number of ears perplant (EAR), located on chromosomes 3, 5, and 6(Table 3 ; Figure 3). These loci, from tolerant parentSD34, caused an increase in EAR, and revealed over-dominance, complete dominance, and additive effect,

94

Table 3 . QTL summary of the F3 families from cross SD34 x SD35 grown in two replications under drought

Chromosome

Marker interval

Closestmarker

GYD1 umc 164 - umc 167 umc763

bnl5.37 -umc96

umcl65

bnl6.25 - umc43

bnl6.255 umc 126 - umc 104 bnl5 .248 umc 124 - umc93 umc 120Total

ASI1 umc58 - bnl5.59 umc 1403

umc60 - umc96

umc966 umc65 - umc62 umc46Total

EAR3

umc6O -umc96

umcl65 umc 126 - umc 104 bn15.246 umc46 -umcl32 umcl32Total

'Estimates are based on a model fitting all putative QTLs simultaneously .bDirection of the additive effect, i .e., if SD34 or SD35 alleles increased the value of the trait under study .

respectively . The phenotypic variance of EAR ranged

Discussionfrom 9.4% to 19.1% with a total of 34 .5% for the threeQTLs simultaneously.

Paterson et al. (1991) pointed out that QTLs affect-ing different traits fell near one another more frequent-ly than would be expected by chance. This suggeststhat the observed strong correlations between GYDand both ASI and EAR (Table 2) may be partly dueto pleiotropic effects of particular QTLs . In fact, oneof the chromosomal regions affecting DYD, ASI, andEAR is on the same marker interval on chromosome 3 .However, only one of the three QTLs affecting EARhas a support interval overlapping with a QTL affectingGYD and with a QTL affecting ASI on chromosomes5 and 6, respectively. This suggests that either certainQTLs have pleiotropic effects (Gruneberg, 1938) onGYD, ASI, and EAR, or that different QTLs affectingthese traits tend to be clustered together into closely-linked groups. Pleiotrophy or close linkage betweenGYD, ASI, and EAR has been suggested by numerousother studies using classical analysis (Fischer et al .,1989; Edmeades et al., 1990; Guei & Wassom, 1992) .

In the F2 generation of a maize cross SD34 x SD35(identified as being drought tolerant and intolerantinbreds, respectively), RFLPs have been used to studyand map the genomic regions of several quantitativelyinherited traits related to drought tolerance . As wasobvious from Figure 2, there is a good coverage ofthe RFLP markers for the maize genome . In total, theQTLs which could be mapped accounted for nearlyhalf of the observed total phenotypic variance of grainyield under drought, 38% in anthesis-silking interval,and 35 % in number of ears per plant. It was not surpris-ing that we were able to explain so much more variationin yield than the other traits, since the parents were themost extreme phenotypes for grain yield under drought(Figure 1) . The remaining variation, which could notbe explained by the QTL models, comes from at leastfour sources : (1) environment plus measurement error,(2) number of loci with smaller phenotypic effect, (3)interactions between QTLs, which were generally toosmall to detect but could still contribute to phenotypicvariance, and (4) interactions of F3 families with envi-ronmental variation (Paterson et al ., 1991 ; Reiter et al .,1991) .

PeakLODscore

Phenotypicvariance%

Genetic effects Directionbadd . dom .

7 .1 22 .4 168 .6 - 89.3 SD345 .3 14 .8 90.2 2.1 SD353 .2 10 .7 152 .5 - 58 .0 SD344.2 13 .9 73 .1 - 12.6 SD343 .9 9 .5 82.8 -113 .4 SD34

49 .6 567 .2 -270.6

4 .6 16 .3 0.25 -0.12 SD354 .5 15 .1 0.55 0.00 SD353 .9 11 .2 0.80 -0.99 SD34

38 .4 1 .60 -1 .11

3 .1 11 .6 0.10 -0.14 SD345 .1 9 .4 0.16 0.18 SD343 .3 19 .1 0.13 0.01 SD34

34 .5 0.39 0.05

The eleven QTLs were distributed on five chromo-somes. Two loci linked to umc76 and umc16 influ-enced grain yield under drought, while number of earsper plant was strongly affected by a locus linked tobnl5 .24. The remaining eight QTLs affected grainyield, anthesis-silking interval, and ears per plantequally . On chromosome 3, the likelihood peaks for theputative QTLs for all three traits fell in the same mark-er interval. The maximum QTLs LOD score for theyield and ears per plant fell in the same region on chro-mosome 5, while anthesis-silking interval and num-ber of ears were affected by QTLs located also in thesame RFLP interval on chromosome 6 . QTL mappingresults of grain yield as affected by anthesis-silkinginterval and ears per plant gave strong evidence ofan interrelation of the genes regulating the three traitsin these genomic regions . The fact that marker inter-vals were found to control these traits agreed with therelatively high phenotypic correlations (Table 2) andrevealed that the QTLs for ear number and anthesis-silking interval would automatically have an effect ongrain yield under water shortage . The phenomenonof significant associations of molecular markers withmore than one trait has also been observed in oth-er studies. Paterson et al. (1991) found overlappingmarker intervals for the highly significantly correlat-ed traits mass per fruit and soluble solids in tomato .Abler et al . (1991) observed the same for yield com-ponents and morphological traits in maize . Lebreton etal. (1995) have observed that the drought stressed leafABA and root pulling force were determined by thesame region on chromosome 3 . Explanations for theseare (i) pleiotropy of the same gene or (ii) effects of twoor more tightly linked genes .

F3 families were evaluated only under droughtstress and were not evaluated under adequate-waterconditions ; thus, the detected QTLs may be importantfor vegetative vigour and may not be unique to adapta-tion to low-water conditions. However, the parentallines SD34 and SD35 had similar equivalent grainyields production under non-stress conditions (1 .37and 1 .28 ton/ha, respectively) . Therefore, allelic dif-ferences between parents did not result in any differ-ence in yield potential under adequate water irrigation .Even thought SD35 is known to be highly intolerant todrought, the tolerance allele at one of the five putativeQTLs for grain yield under drought was contributed bythis inbred .

The use of F3 lines for QTL mapping has a numberof advantages in comparison with other generations .First, utilization of replicated progenies for pheno-

95

typing leads to an increase in the power for testinghypotheses about genetic effects of QTLs (Knapp andBridges 1990) . Second, in contrast to backcross or ful-ly homozygous progenies (e.g ., recombinant inbredsor double haploid), F3 lines allow dominance effectsto be estimated (Schon et al ., 1993). However, thepower of testing for dominance effects is reduced inF3 lines compared with F2 individuals . This is becausedominance effects contribute only half to the genotypicmean of the heterozygous marker class in the case ofF3 lines compared to F2 individuals (Mather & Jinks,1971) .

For the maize breeder, overdominance or domi-nance for tolerance to drought stress is the preferabletype of gene action . Sch(n et al . (1993) showed thatif the resistance of an inbred is fully expressed in thehybrid, the second parent of a cross can be chosenaccording to other criteria . Transmission of drought-tolerance in maize from resistant inbreds to hybridcombinations was investigated by Sharma & Bhalla(1991). Guei & Wassom (1992) found that additivegene action was more important than dominance incontrolling the expression of flowering traits, whiledominance was more important for yield and ears perplant.

Under stress conditions, selection for secondarytraits correlated to grain yield, such as ASI andEAR, which has relatively high heritability, appear toincrease selection efficiency (Edmeades et al ., 1990).Even if ASI is a relatively simple trait to measure inthe field, selection for short ASI, if carried out "con-ventionally", requires proper drought conditions dur-ing each cycle which severely limits its use in manybreeding programs (Ribaut et al ., 1995). The use ofmolecular markers to improve the efficiency of breed-ing towards better drought tolerance may provide aworking alternative .

Contemplation of marker assisted breeding shouldbe done on a case-by-case basis . Further investigationfor drought tolerance will be required to establish theimportance of the identified genomic regions in otherbackgrounds . Field evaluation is also required to estab-lish the effectiveness of the drought screening systemin modeling water stress responses and in evaluatingthe stability of QTLs across environments (Reiter et al .,1991). Our results indicate the existence of genes orgene clusters with major effects involved in the controlof significant proportions of the phenotypic variationin quantitatively inherited traits related to drought tol-erance such as grain yield, anthesis-silking interval andnumber of ears per plant. The identification of marker

96

loci linked to QTLs involved in drought tolerance isan important step in the genotypic evaluation of maizegermplasm . Phenotypic selection for drought tolerancemay not be difficult, but identified marker loci may beuseful in multiple-trait selection where drought toler-ance is one of many traits of interest .

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